USING FUZZY SET BASED MIND MAPS FOR PROFESSIONAL LIFE-LONG LEARNING: A CASE STUDY FOR M-LEARNING

As the amount of knowledge about technological developments grow exponentially, the need for an intuitive method to keep up with the latest technological trends is becoming ever so important. This special type of professional life-long learning can be addressed by a just-in-time knowledge management (JITKM) application. This paper presents the evaluation of a JITKM application called the Mind Map And Fuzzy Set Information Retrieval System (MFIRS) [1]. MFIRS combines fuzzy set information retrieval paradigm with the semantics and structure of a mind map to filter and sort RSS feeds from the Internet. The user draws a mind map using commonly available tools and the MFIRS scours the web for RSS feeds related to the mind map, and sorts and presents the results to the end-user based on the structure and content of the mind map. The key aspect of this tool is an ability to match user’s interests embedded in the mind map with the ordering of the RSS feed results. This paper presents a case study where researchers in the area of mobile and ubiquitous learning [2, 3] were chosen as the potential clients of this tool. Based on e-Learning themes outlined in [4], several mobile and ubiquitous learning mind maps for research related issues were drawn by experts. These mind maps were used as the basis of creating a JITKMS using Google Alerts and Science Direct Academic databases as the raw resources for recent RSS feeds related to mobile and ubiquitous learning. Over 800 active researchers in the area of mobile and ubiquitous learning were identified based on current publications in key conferences (e.g., WMUTE) and journals. These researchers were randomly divided into control and treatment groups. One set of researchers received RSS feeds sorted through the mind maps, and the other received the original sort order through e-mails. Each researcher was asked to review the quality of the results from each mind map. In addition, the willingness of these researchers to adopt such a JITKMS was judged based on the UTAUT-based adoption models [5, 6, 7].